Bibliographic record
Abstract
PURPOSE OF REVIEW: The approach of active surveillance for low-risk prostate cancer has evolved in many ways since its introduction 20 years ago. There is a great deal of ongoing research addressing the molecular genetics and clinical outcome of low-risk disease, the use of MRI and biomarkers, and the role of lifestyle and dietary modifications. The major developments in the field are reviewed in this article. RECENT FINDINGS: Low risk and many cases of low-intermediate risk prostate cancer are indolent, have little or no metastatic potential, and do not pose a threat to the patient in his lifetime. These are termed clinically insignificant. Studies over the last 20 years have advanced our understanding of who these patients are, and promoted the use of conservative management in such individuals. A key component of this approach is the early identification of those patients who have been misattributed as having low-risk disease, who in fact harbor higher risk disease and are likely to benefit from definitive therapy. This represents about 30% of newly diagnosed low-risk patients. A further small proportion of patients with low-risk disease demonstrate biological progression to higher grade disease. Extent of Gleason 6 on biopsy, Prostate Specific Antigen density, and race are predictors for the likelihood of coexistent higher grade cancer. SUMMARY: The results of active surveillance, embodying conservative management with selective, delayed intervention for the subset who are reclassified as higher risk over time based on repeat biopsy, imaging, or biomarker results, have shown that this approach is safe in the intermediate to long term, with a 0.5-3% cancer-specific mortality at 10-15 years. Further refinement incorporating MRI and targeted biopsies is the subject of intensive research at the moment, and promises to improve the safety and precision of conservative management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".